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数值化

ds2训练分类器

ds2 -> ds1

predict_prop_ds1 <- matrix(data=predict_ds1_test, nrow = length(levels(Idents(ds2))), 
                           ncol = ncol(ds1), byrow = FALSE, 
                           dimnames = list(levels(Idents(ds2)),colnames(ds1)))

## 得到分群结果
ds1_res <- apply(predict_prop_ds1,2,func,rownames(predict_prop_ds1))
Idents(ds1) <- factor(ds1_res,levels = c(0:4))
umapplot(ds1)

数值化地投射回umap

embedding <- FetchData(object = ds1, vars = c("UMAP_1", "UMAP_2"))
embedding <- cbind(embedding, t(predict_prop_ds1))

ggobj <- ggplot() +
  geom_point(data = embedding[embedding$`0`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `0`), shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('0', low = "#FFFFFF00", high = "#6dc0a6") +
  new_scale("color") +
    geom_point(data = embedding[embedding$`1`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `1`), size = 3, alpha=0.5) + 
  scale_color_gradient('1', low = "#FFFFFF00", high = "#e2b398") +
   new_scale("color") +
    geom_point(data = embedding[embedding$`2`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `2`), size = 2, alpha=0.5) + 
  scale_color_gradient('2', low = "#FFFFFF00", high = "#e2a2ca") +
  new_scale("color") +
    geom_point(data = embedding[embedding$`3`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `3`), size = 3, alpha=0.5) + 
  scale_color_gradient('3', low = "#FFFFFF00", high = "#d1eba8") +
   new_scale("color") +
      geom_point(data = embedding[embedding$`4`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `4`), size = 3, alpha=0.5) + 
  scale_color_gradient('4', low = "#FFFFFF00", high = "#b1d6fb") +
    new_scale("color") +
        xlab("UMAP 1") + ylab("UMAP 2")  +
        theme(axis.line = element_line(arrow = arrow(length = unit(0.2, "cm")))) +
        scale_y_continuous(breaks = NULL) +
        scale_x_continuous(breaks = NULL) + 
  theme(panel.background = element_blank(), panel.grid = element_blank(), legend.position = "bottom")
ggsave("pre_ds1_umap.svg",device = svg,plot = ggobj,height = 10,width = 10)

#ds2 -> ds0

Idents(ds0) <- ds0$seurat_clusters
temp <- get_data_table(ds0, highvar = F, type = "data")
ds0_data <- matrix(data=0, nrow = length(rownames(ds2_data)), ncol = length(colnames(temp)), 
                   byrow = FALSE, dimnames = list(rownames(ds2_data),colnames(temp)))
for(i in intersect(rownames(ds2_data), rownames(temp))){
  ds0_data[i,] <- temp[i,]
}
rm(temp)
ds0_label <- as.numeric(as.character(Idents(ds0)))
colnames(ds0_data) <- NULL
ds0_test_data <- list(data = t(as(ds0_data,"dgCMatrix")), label = ds0_label)
ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)

#预测结果

predict_ds0_test <- predict(bst_model, newdata = ds0_test)

predict_prop_ds0 <- matrix(data=predict_ds0_test, nrow = length(levels(Idents(ds2))), 
                           ncol = ncol(ds0), byrow = FALSE, 
                           dimnames = list(levels(Idents(ds2)),colnames(ds0)))

## 得到分群结果
ds0_res <- apply(predict_prop_ds0,2,func,rownames(predict_prop_ds0))
Idents(ds0) <- factor(ds0_res,levels = c(0:4))
umapplot(ds0)

embedding <- FetchData(object = ds0, vars = c("UMAP_1", "UMAP_2"))
embedding <- cbind(embedding, t(predict_prop_ds0))

ggobj <- ggplot() +
  geom_point(data = embedding[embedding$`0`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `0`), shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('0', low = "#FFFFFF00", high = "#6dc0a6") +
  new_scale("color") +
    geom_point(data = embedding[embedding$`1`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `1`),shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('1', low = "#FFFFFF00", high = "#e2b398") +
   new_scale("color") +
    geom_point(data = embedding[embedding$`2`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `2`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('2', low = "#FFFFFF00", high = "#e2a2ca") +
  new_scale("color") +
    geom_point(data = embedding[embedding$`3`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `3`),shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('3', low = "#FFFFFF00", high = "#d1eba8") +
   new_scale("color") +
      geom_point(data = embedding[embedding$`4`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `4`),shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('4', low = "#FFFFFF00", high = "#b1d6fb") +
    new_scale("color") +
        xlab("UMAP 1") + ylab("UMAP 2")  +
        theme(axis.line = element_line(arrow = arrow(length = unit(0.2, "cm")))) +
        scale_y_continuous(breaks = NULL) +
        scale_x_continuous(breaks = NULL) + 
  theme(panel.background = element_blank(), panel.grid = element_blank(), legend.position = "bottom")
ggsave("pre_ds1_umap.svg",device = svg,plot = ggobj,height = 10,width = 10)

PA -> AC

Idents(ds2_PA) <- ds2_PA$seurat_clusters
selected_features <- read.csv("./datatable/selected_features.csv", stringsAsFactors = F)
selected_features <- selected_features$x
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)
PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softprob", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, PA_train, nrounds = 100, verbose = 0)

embedding <- FetchData(object = ds2_AC, vars = c("UMAP_1", "UMAP_2"))
embedding <- cbind(embedding, t(predict_prop_AC))

ggobj <- ggplot() +
  geom_point(data = embedding[embedding$`0`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `0`), shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('0', low = "#FFFFFF00", high = "#6dc0a6") +
  new_scale("color") +
    geom_point(data = embedding[embedding$`1`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `1`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('1', low = "#FFFFFF00", high = "#e2b398") +
   new_scale("color") +
    geom_point(data = embedding[embedding$`2`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `2`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('2', low = "#FFFFFF00", high = "#e2a2ca") +
        xlab("UMAP 1") + ylab("UMAP 2")  +
        theme(axis.line = element_line(arrow = arrow(length = unit(0.2, "cm")))) +
        scale_y_continuous(breaks = NULL) +
        scale_x_continuous(breaks = NULL) + 
  theme(panel.background = element_blank(), panel.grid = element_blank(), legend.position = "bottom")
ggsave("ds2_PAtoAC_umap.svg",device = svg,plot = ggobj,height = 8,width = 8)

在ds0上训练

ds0_data <- get_data_table(ds0, highvar = F, type = "data")
ds0_label <- as.numeric(as.character(Idents(ds0)))

index <- c(1:dim(ds0_data)[2]) %>% sample(ceiling(0.3*dim(ds0_data)[2]), replace = F, prob = NULL)
colnames(ds0_data) <- NULL

ds0_train_data <- list(data = t(as(ds0_data[,-index],"dgCMatrix")), label = ds0_label[-index])
ds0_test_data <- list(data = t(as(ds0_data[,index],"dgCMatrix")), label = ds0_label[index])

ds0_train <- xgb.DMatrix(data = ds0_train_data$data,label = ds0_train_data$label)
ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)

watchlist <- list(train = ds0_train, eval = ds0_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds0))),
                  objective = "multi:softprob", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, ds0_train, nrounds = 100, watchlist, verbose = 0)

eval_loss <- bst_model[["evaluation_log"]][["eval_mlogloss"]]
plot_ly(data.frame(eval_loss), x = c(1:100), y = eval_loss) %>% 
  add_trace(type = "scatter", mode = "markers+lines", 
            marker = list(color = "black", line = list(color = "#1E90FFC7", width = 1)),
            line = list(color = "#1E90FF80", width = 2)) %>% 
  layout(xaxis = list(title = "epoch"),yaxis = list(title = "eval_mlogloss"))

importance <- xgb.importance(colnames(ds0_train), model = bst_model)
head(importance)

xgb.ggplot.importance(head(importance,20), n_clusters = 1) + theme_minimal()

multi_featureplot(head(importance,9)$Feature, ds0) 

Idents(ds2) <- ds2$seurat_clusters 
temp <- get_data_table(ds2, highvar = F, type = "data")
ds2_data <- matrix(data=0, nrow = length(rownames(ds0_data)), ncol = length(colnames(temp)), 
                   byrow = FALSE, dimnames = list(rownames(ds0_data),colnames(temp)))
for(i in intersect(rownames(ds2_data), rownames(temp))){
  ds2_data[i,] <- temp[i,]
}
rm(temp)
ds2_label <- as.numeric(as.character(Idents(ds2)))
colnames(ds2_data) <- NULL
ds2_test_data <- list(data = t(as(ds2_data,"dgCMatrix")), label = ds2_label)
ds2_test <- xgb.DMatrix(data = ds2_test_data$data,label = ds2_test_data$label)

#预测结果

predict_ds2_test <- predict(bst_model, newdata = ds2_test)

predict_prop_ds2 <- matrix(data=predict_ds2_test, nrow = bst_model[["params"]][["num_class"]], 
                           ncol = ncol(ds2), byrow = FALSE, 
                           dimnames = list(c(0:(bst_model[["params"]][["num_class"]]-1)),colnames(ds2)))

## 得到分群结果
ds2_res <- apply(predict_prop_ds2,2,func,rownames(predict_prop_ds2))
confuse_matrix1 <- table(ds2_test_data$label, ds2_res, dnn=c("true","pre"))

sankey_plot(confuse_matrix1,0:5,0:4,session = "ds0tods2")

Idents(ds2) <- factor(ds2_res,levels = c(0:5))
umapplot(ds2)

embedding <- FetchData(object = ds2, vars = c("UMAP_1", "UMAP_2"))
embedding <- cbind(embedding, t(predict_prop_ds2))

ggobj <- ggplot() +
  geom_point(data = embedding[embedding$`0`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `0`), shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('0', low = "#FFFFFF00", high = "#6dc0a6") +
  new_scale("color") +
    geom_point(data = embedding[embedding$`1`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `1`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('1', low = "#FFFFFF00", high = "#e2b398") +
   new_scale("color") +
    geom_point(data = embedding[embedding$`2`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `2`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('2', low = "#FFFFFF00", high = "#e2a2ca") +
     new_scale("color") +
    geom_point(data = embedding[embedding$`3`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `3`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('3', low = "#FFFFFF00", high = "#d1eba8") +
     new_scale("color") +
    geom_point(data = embedding[embedding$`4`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `4`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('4', low = "#FFFFFF00", high = "#b1d6fb") +
     new_scale("color") +
    geom_point(data = embedding[embedding$`5`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `5`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('5', low = "#FFFFFF00", high = "#fd9999") +
        xlab("UMAP 1") + ylab("UMAP 2")  +
        theme(axis.line = element_line(arrow = arrow(length = unit(0.2, "cm")))) +
        scale_y_continuous(breaks = NULL) +
        scale_x_continuous(breaks = NULL) + 
  theme(panel.background = element_blank(), panel.grid = element_blank(), legend.position = "bottom")
ggsave("ds0tods2umap.svg",device = svg,plot = ggobj,height = 8,width = 8)

embedding <- FetchData(object = ds1, vars = c("UMAP_1", "UMAP_2"))
embedding <- cbind(embedding, t(predict_prop_ds1))

ggobj <- ggplot() +
  geom_point(data = embedding[embedding$`0`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `0`), shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('0', low = "#FFFFFF00", high = "#6dc0a6") +
  new_scale("color") +
    geom_point(data = embedding[embedding$`1`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `1`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('1', low = "#FFFFFF00", high = "#e2b398") +
   new_scale("color") +
    geom_point(data = embedding[embedding$`2`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `2`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('2', low = "#FFFFFF00", high = "#e2a2ca") +
     new_scale("color") +
    geom_point(data = embedding[embedding$`3`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `3`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('3', low = "#FFFFFF00", high = "#d1eba8") +
     new_scale("color") +
    geom_point(data = embedding[embedding$`4`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `4`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('4', low = "#FFFFFF00", high = "#b1d6fb") +
     new_scale("color") +
    geom_point(data = embedding[embedding$`5`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `5`),shape=16, size = 2, alpha=0.5) + 
  scale_color_gradient('5', low = "#FFFFFF00", high = "#fd9999") +
        xlab("UMAP 1") + ylab("UMAP 2")  +
        theme(axis.line = element_line(arrow = arrow(length = unit(0.2, "cm")))) +
        scale_y_continuous(breaks = NULL) +
        scale_x_continuous(breaks = NULL) + 
  theme(panel.background = element_blank(), panel.grid = element_blank(), legend.position = "bottom")
ggsave("ds0tods1umap.svg",device = svg,plot = ggobj,height = 8,width = 8)

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